Multiple path prediction for traffic scenes using LSTMs and mixture density models

This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye vie

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Moving object path prediction for traffic scenes

Accurate and efficient inference and prediction are important elements in intelligent systems. Knowing in advance the behaviour of an entity, such as the price of a product in the future, the weather in the next few days or the position of an object in the near future, is important for several applications like stock market, weather forecasting, robotics and more recently for autonomous vehicles. The aim of this work is to investigate and develop a novel approach for predicting the path of moving objects such as pedestrians and vehicles in the context of ego-cameras, like those mounted on a vehicle or a person. Due to the sequential nature of the data presented in paths, Recurrent Neural Networks (RNNs) are exploited, specifically Long Short-Term Memory Networks (LSTMs), due to their ability to process this type of data. LSTMs have the limitation of only predicting a single path per tracklet. Path prediction requires predicting with a level of uncertainty. Predicting multiple future paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was achieved by combining LSTMs and MDNs. One of the objectives of this work is to include more information than simple position in the path prediction task, such as velocity of the ego vehicle and contextual information of the surroundings. Though the main interest of this work is on egocentric cameras experiments were also conducted using fixed cameras for a surveillance perspective. Two public datasets were used: KITTI and CityFlow. In summary, this thesis extends moving object path prediction methods in the context of traffic scenes for objects such as pedestrians, vehicles, cyclists

Moving object path prediction in traffic scenes using contextual information

Abstract: Moving object path prediction in traffic scenes from the perspective of a moving vehicle can improve safety on the road, which is the aim of Advanced Driver Assistance Systems (ADAS). However, this task still remains a challenge. Work has been carried out on the use of x,y positional information of the moving objects only. However, besides positional information there is more information that surrounds a vehicle that can be leveraged in the prediction along with the x, y features. This is known as contextual information. In this work, a deep exploration of these features is carried out by evaluating different types of data, using different fusion strategies. The core architectures of this model are CNN and LSTM architectures. It is concluded that in the prediction task, not only are the features important, but the way they are fused in the developed architecture is also of importance